Optimization of Replanning Processes for Volumetric Modulated Arc Therapy Plans at Risk of QA Failure Predicted by a Machine Learning Model

Abstract

Patient-specific quality assurance (PSQA) procedures ensure the safe delivery of volumetric modulated arc therapy (VMAT) plans. PSQA requires extensive time and resources and may cause treatment delays if replanning is needed due to failures. Recently, our group developed a machine learning (ML) model predicting gamma passing rate (GPR) for VMAT arcs. This study explores automatable replanning strategies for plans identified at risk of failure, aiming to improve deliverability while maintaining dosimetric quality. Between 2022 and 2023, our ML model analyzed 1252 VMAT plans. Ten patients having a predicted GPR (pGPR) <95% were selected. Replanning strategies consisted of limiting monitor units (MUlimit) and employing the aperture shape controller (ASC) tool. Re-optimized plans were compared with the originals in terms of dose volume constraints (DVCs) for the target and organs-at-risk (OARs), and deliverability using the modulation complexity score (MCS), pGPR, and measured GPR (mGPR). Forty-five re-optimizations were performed. Replanning led to an increase in DVCs for OARs and a reduction for the target. Complexity decreased, reflected by the increase in the MCS from 0.17 to 0.21 (MUlimit) and 0.20 (ASC). The deliverability improved, with the pGPR increasing from 93.3% to 94.4% (MUlimit) and 95.1% (ASC), and the mGPR from 99.3% to 99.7% (MUlimit) and 99.8% (ASC). Limiting the MUs or utilizing the ASC reduced the complexity of plans and improved the GPR without compromising the dosimetric quality. These strategies can be used to automate replanning procedures, reduce the workload related to PSQA, and improve patient safety.

Keywords

• patient-specific QA
• machine learning
• automation
• radiotherapy
• plan quality